Overview

Dataset statistics

Number of variables8
Number of observations42
Missing cells120
Missing cells (%)35.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory67.1 B

Variable types

Text6
Unsupported1
Categorical1

Alerts

전북 지역자활센터 현황 has 23 (54.8%) missing valuesMissing
Unnamed: 1 has 15 (35.7%) missing valuesMissing
Unnamed: 2 has 19 (45.2%) missing valuesMissing
Unnamed: 3 has 19 (45.2%) missing valuesMissing
Unnamed: 4 has 5 (11.9%) missing valuesMissing
Unnamed: 5 has 20 (47.6%) missing valuesMissing
Unnamed: 6 has 19 (45.2%) missing valuesMissing
Unnamed: 3 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-14 02:25:12.657038
Analysis finished2024-03-14 02:25:13.386151
Duration0.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

전북 지역자활센터 현황
Text

MISSING 

Distinct19
Distinct (%)100.0%
Missing23
Missing (%)54.8%
Memory size468.0 B
2024-03-14T11:25:13.491702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.1578947
Min length1

Characters and Unicode

Total characters41
Distinct characters33
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)100.0%

Sample

1st row시군
2nd row계
3rd row전주
4th row군산
5th row익산
ValueCountFrequency (%)
시군 1
 
5.0%
계 1
 
5.0%
활 1
 
5.0%
자 1
 
5.0%
청소년 1
 
5.0%
부안 1
 
5.0%
고창 1
 
5.0%
순창 1
 
5.0%
임실 1
 
5.0%
장수 1
 
5.0%
Other values (10) 10
50.0%
2024-03-14T11:25:13.775203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 3
 
7.3%
안 2
 
4.9%
원 2
 
4.9%
2
 
4.9%
군 2
 
4.9%
산 2
 
4.9%
창 2
 
4.9%
실 1
 
2.4%
순 1
 
2.4%
고 1
 
2.4%
Other values (23) 23
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 39
95.1%
Space Separator 2
 
4.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 3
 
7.7%
안 2
 
5.1%
원 2
 
5.1%
군 2
 
5.1%
산 2
 
5.1%
창 2
 
5.1%
실 1
 
2.6%
순 1
 
2.6%
고 1
 
2.6%
부 1
 
2.6%
Other values (22) 22
56.4%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 39
95.1%
Common 2
 
4.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 3
 
7.7%
안 2
 
5.1%
원 2
 
5.1%
군 2
 
5.1%
산 2
 
5.1%
창 2
 
5.1%
실 1
 
2.6%
순 1
 
2.6%
고 1
 
2.6%
부 1
 
2.6%
Other values (22) 22
56.4%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 39
95.1%
ASCII 2
 
4.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 3
 
7.7%
안 2
 
5.1%
원 2
 
5.1%
군 2
 
5.1%
산 2
 
5.1%
창 2
 
5.1%
실 1
 
2.6%
순 1
 
2.6%
고 1
 
2.6%
부 1
 
2.6%
Other values (22) 22
56.4%
ASCII
ValueCountFrequency (%)
2
100.0%

Unnamed: 1
Text

MISSING 

Distinct23
Distinct (%)85.2%
Missing15
Missing (%)35.7%
Memory size468.0 B
2024-03-14T11:25:14.185532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.2592593
Min length2

Characters and Unicode

Total characters61
Distinct characters39
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)77.8%

Sample

1st row센터명
2nd row19개소
3rd row전북
4th row광역
5th row전주
ValueCountFrequency (%)
전주 4
 
14.3%
정읍 2
 
7.1%
센터명 1
 
3.6%
고창 1
 
3.6%
순창 1
 
3.6%
임실 1
 
3.6%
장수 1
 
3.6%
무주 1
 
3.6%
진안 1
 
3.6%
완주 1
 
3.6%
Other values (14) 14
50.0%
2024-03-14T11:25:14.423612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 6
 
9.8%
전 5
 
8.2%
산 4
 
6.6%
정 2
 
3.3%
읍 2
 
3.3%
익 2
 
3.3%
원 2
 
3.3%
광 2
 
3.3%
안 2
 
3.3%
창 2
 
3.3%
Other values (29) 32
52.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 58
95.1%
Decimal Number 2
 
3.3%
Space Separator 1
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 6
 
10.3%
전 5
 
8.6%
산 4
 
6.9%
정 2
 
3.4%
읍 2
 
3.4%
익 2
 
3.4%
원 2
 
3.4%
광 2
 
3.4%
안 2
 
3.4%
창 2
 
3.4%
Other values (26) 29
50.0%
Decimal Number
ValueCountFrequency (%)
9 1
50.0%
1 1
50.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 58
95.1%
Common 3
 
4.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 6
 
10.3%
전 5
 
8.6%
산 4
 
6.9%
정 2
 
3.4%
읍 2
 
3.4%
익 2
 
3.4%
원 2
 
3.4%
광 2
 
3.4%
안 2
 
3.4%
창 2
 
3.4%
Other values (26) 29
50.0%
Common
ValueCountFrequency (%)
1
33.3%
9 1
33.3%
1 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 58
95.1%
ASCII 3
 
4.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 6
 
10.3%
전 5
 
8.6%
산 4
 
6.9%
정 2
 
3.4%
읍 2
 
3.4%
익 2
 
3.4%
원 2
 
3.4%
광 2
 
3.4%
안 2
 
3.4%
창 2
 
3.4%
Other values (26) 29
50.0%
ASCII
ValueCountFrequency (%)
1
33.3%
9 1
33.3%
1 1
33.3%

Unnamed: 2
Text

MISSING 

Distinct21
Distinct (%)91.3%
Missing19
Missing (%)45.2%
Memory size468.0 B
2024-03-14T11:25:14.567680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3
Min length2

Characters and Unicode

Total characters69
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)82.6%

Sample

1st row센터장
2nd row공석
3rd row허종현
4th row박준홍
5th row조용희
ValueCountFrequency (%)
김복례 2
 
8.7%
허종현 2
 
8.7%
최우영 1
 
4.3%
센터장 1
 
4.3%
김진왕 1
 
4.3%
제춘홍 1
 
4.3%
한승연 1
 
4.3%
직무대행 1
 
4.3%
김종수 1
 
4.3%
서정일 1
 
4.3%
Other values (11) 11
47.8%
2024-03-14T11:25:14.853658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 5
 
7.2%
종 3
 
4.3%
진 3
 
4.3%
홍 2
 
2.9%
승 2
 
2.9%
복 2
 
2.9%
최 2
 
2.9%
용 2
 
2.9%
장 2
 
2.9%
례 2
 
2.9%
Other values (42) 44
63.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 69
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 5
 
7.2%
종 3
 
4.3%
진 3
 
4.3%
홍 2
 
2.9%
승 2
 
2.9%
복 2
 
2.9%
최 2
 
2.9%
용 2
 
2.9%
장 2
 
2.9%
례 2
 
2.9%
Other values (42) 44
63.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 69
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 5
 
7.2%
종 3
 
4.3%
진 3
 
4.3%
홍 2
 
2.9%
승 2
 
2.9%
복 2
 
2.9%
최 2
 
2.9%
용 2
 
2.9%
장 2
 
2.9%
례 2
 
2.9%
Other values (42) 44
63.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 69
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 5
 
7.2%
종 3
 
4.3%
진 3
 
4.3%
홍 2
 
2.9%
승 2
 
2.9%
복 2
 
2.9%
최 2
 
2.9%
용 2
 
2.9%
장 2
 
2.9%
례 2
 
2.9%
Other values (42) 44
63.8%

Unnamed: 3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing19
Missing (%)45.2%
Memory size468.0 B

Unnamed: 4
Text

MISSING 

Distinct36
Distinct (%)97.3%
Missing5
Missing (%)11.9%
Memory size468.0 B
2024-03-14T11:25:15.175210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length20
Mean length15.567568
Min length2

Characters and Unicode

Total characters576
Distinct characters114
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)94.6%

Sample

1st row주소
2nd row전주시 덕진구 금암동 760-1
3rd row전주시 완산구 현무1길 21-15
4th row3층 (경원동3가 179-9)
5th row전주시 완산구 팔달로 212-3
ValueCountFrequency (%)
전주시 5
 
3.9%
3층 5
 
3.9%
2층 4
 
3.1%
완산구 3
 
2.3%
익산시 2
 
1.6%
경원동3가 2
 
1.6%
금암동 2
 
1.6%
군산시 2
 
1.6%
정읍시 2
 
1.6%
현무1길 2
 
1.6%
Other values (97) 99
77.3%
2024-03-14T11:25:15.537780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
101
 
17.5%
1 34
 
5.9%
2 23
 
4.0%
- 20
 
3.5%
3 19
 
3.3%
) 17
 
3.0%
( 17
 
3.0%
동 17
 
3.0%
시 14
 
2.4%
로 13
 
2.3%
Other values (104) 301
52.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 274
47.6%
Decimal Number 140
24.3%
Space Separator 101
 
17.5%
Dash Punctuation 20
 
3.5%
Close Punctuation 17
 
3.0%
Open Punctuation 17
 
3.0%
Uppercase Letter 6
 
1.0%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 17
 
6.2%
시 14
 
5.1%
로 13
 
4.7%
군 11
 
4.0%
층 10
 
3.6%
읍 10
 
3.6%
주 9
 
3.3%
길 8
 
2.9%
산 8
 
2.9%
리 8
 
2.9%
Other values (84) 166
60.6%
Decimal Number
ValueCountFrequency (%)
1 34
24.3%
2 23
16.4%
3 19
13.6%
4 13
 
9.3%
5 11
 
7.9%
0 9
 
6.4%
9 8
 
5.7%
7 8
 
5.7%
6 8
 
5.7%
8 7
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
A 2
33.3%
S 1
16.7%
Y 1
16.7%
M 1
16.7%
C 1
16.7%
Space Separator
ValueCountFrequency (%)
101
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 296
51.4%
Hangul 274
47.6%
Latin 6
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 17
 
6.2%
시 14
 
5.1%
로 13
 
4.7%
군 11
 
4.0%
층 10
 
3.6%
읍 10
 
3.6%
주 9
 
3.3%
길 8
 
2.9%
산 8
 
2.9%
리 8
 
2.9%
Other values (84) 166
60.6%
Common
ValueCountFrequency (%)
101
34.1%
1 34
 
11.5%
2 23
 
7.8%
- 20
 
6.8%
3 19
 
6.4%
) 17
 
5.7%
( 17
 
5.7%
4 13
 
4.4%
5 11
 
3.7%
0 9
 
3.0%
Other values (5) 32
 
10.8%
Latin
ValueCountFrequency (%)
A 2
33.3%
S 1
16.7%
Y 1
16.7%
M 1
16.7%
C 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 302
52.4%
Hangul 274
47.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
101
33.4%
1 34
 
11.3%
2 23
 
7.6%
- 20
 
6.6%
3 19
 
6.3%
) 17
 
5.6%
( 17
 
5.6%
4 13
 
4.3%
5 11
 
3.6%
0 9
 
3.0%
Other values (10) 38
 
12.6%
Hangul
ValueCountFrequency (%)
동 17
 
6.2%
시 14
 
5.1%
로 13
 
4.7%
군 11
 
4.0%
층 10
 
3.6%
읍 10
 
3.6%
주 9
 
3.3%
길 8
 
2.9%
산 8
 
2.9%
리 8
 
2.9%
Other values (84) 166
60.6%

Unnamed: 5
Text

MISSING 

Distinct22
Distinct (%)100.0%
Missing20
Missing (%)47.6%
Memory size468.0 B
2024-03-14T11:25:15.698826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.7272727
Min length2

Characters and Unicode

Total characters170
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row전화
2nd row226-0388
3rd row283-9766
4th row232-8383
5th row272-2845
ValueCountFrequency (%)
632-4747 1
 
4.5%
226-0388 1
 
4.5%
544-9005 1
 
4.5%
288-9005 1
 
4.5%
583-0045 1
 
4.5%
562-2014 1
 
4.5%
653-0921 1
 
4.5%
642-4840 1
 
4.5%
352-7179 1
 
4.5%
324-2710 1
 
4.5%
Other values (12) 12
54.5%
2024-03-14T11:25:15.984210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 24
14.1%
4 24
14.1%
- 21
12.4%
3 19
11.2%
0 16
9.4%
5 13
7.6%
8 12
7.1%
7 10
5.9%
9 10
5.9%
1 10
5.9%
Other values (3) 11
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 147
86.5%
Dash Punctuation 21
 
12.4%
Other Letter 2
 
1.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 24
16.3%
4 24
16.3%
3 19
12.9%
0 16
10.9%
5 13
8.8%
8 12
8.2%
7 10
6.8%
9 10
6.8%
1 10
6.8%
6 9
 
6.1%
Other Letter
ValueCountFrequency (%)
전 1
50.0%
화 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 168
98.8%
Hangul 2
 
1.2%

Most frequent character per script

Common
ValueCountFrequency (%)
2 24
14.3%
4 24
14.3%
- 21
12.5%
3 19
11.3%
0 16
9.5%
5 13
7.7%
8 12
7.1%
7 10
6.0%
9 10
6.0%
1 10
6.0%
Hangul
ValueCountFrequency (%)
전 1
50.0%
화 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 168
98.8%
Hangul 2
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 24
14.3%
4 24
14.3%
- 21
12.5%
3 19
11.3%
0 16
9.5%
5 13
7.7%
8 12
7.1%
7 10
6.0%
9 10
6.0%
1 10
6.0%
Hangul
ValueCountFrequency (%)
전 1
50.0%
화 1
50.0%

Unnamed: 6
Text

MISSING 

Distinct15
Distinct (%)65.2%
Missing19
Missing (%)45.2%
Memory size468.0 B
2024-03-14T11:25:16.113037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.4347826
Min length2

Characters and Unicode

Total characters194
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)56.5%

Sample

1st row지정
2nd row일자
3rd row2008.10.1
4th row1998.9.17
5th row2000.8.24
ValueCountFrequency (%)
1 11
32.4%
2001.7 7
20.6%
2000.8.24 3
 
8.8%
지정 1
 
2.9%
일자 1
 
2.9%
2008.10.1 1
 
2.9%
1998.9.17 1
 
2.9%
2003.8 1
 
2.9%
2001.12.31 1
 
2.9%
2001.5.19 1
 
2.9%
Other values (6) 6
17.6%
2024-03-14T11:25:16.362011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 45
23.2%
. 42
21.6%
1 31
16.0%
2 27
13.9%
11
 
5.7%
7 10
 
5.2%
8 7
 
3.6%
3 5
 
2.6%
4 4
 
2.1%
9 4
 
2.1%
Other values (5) 8
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 137
70.6%
Other Punctuation 42
 
21.6%
Space Separator 11
 
5.7%
Other Letter 4
 
2.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 45
32.8%
1 31
22.6%
2 27
19.7%
7 10
 
7.3%
8 7
 
5.1%
3 5
 
3.6%
4 4
 
2.9%
9 4
 
2.9%
5 4
 
2.9%
Other Letter
ValueCountFrequency (%)
지 1
25.0%
정 1
25.0%
일 1
25.0%
자 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 42
100.0%
Space Separator
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 190
97.9%
Hangul 4
 
2.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 45
23.7%
. 42
22.1%
1 31
16.3%
2 27
14.2%
11
 
5.8%
7 10
 
5.3%
8 7
 
3.7%
3 5
 
2.6%
4 4
 
2.1%
9 4
 
2.1%
Hangul
ValueCountFrequency (%)
지 1
25.0%
정 1
25.0%
일 1
25.0%
자 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 190
97.9%
Hangul 4
 
2.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 45
23.7%
. 42
22.1%
1 31
16.3%
2 27
14.2%
11
 
5.8%
7 10
 
5.3%
8 7
 
3.7%
3 5
 
2.6%
4 4
 
2.1%
9 4
 
2.1%
Hangul
ValueCountFrequency (%)
지 1
25.0%
정 1
25.0%
일 1
25.0%
자 1
25.0%

Unnamed: 7
Categorical

Distinct8
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size468.0 B
<NA>
20 
표준
8 
기본
5 
확대
4 
-
 
2
Other values (3)
3 

Length

Max length4
Median length3
Mean length2.9285714
Min length1

Unique

Unique3 ?
Unique (%)7.1%

Sample

1st row규모
2nd row유형
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 20
47.6%
표준 8
 
19.0%
기본 5
 
11.9%
확대 4
 
9.5%
- 2
 
4.8%
규모 1
 
2.4%
유형 1
 
2.4%
표준 1
 
2.4%

Length

2024-03-14T11:25:16.476761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:25:16.570674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 20
47.6%
표준 9
21.4%
기본 5
 
11.9%
확대 4
 
9.5%
2
 
4.8%
규모 1
 
2.4%
유형 1
 
2.4%

Correlations

2024-03-14T11:25:16.645001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전북 지역자활센터 현황Unnamed: 1Unnamed: 2Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7
전북 지역자활센터 현황1.0001.0001.0001.0001.0001.0001.000
Unnamed: 11.0001.0001.0001.0001.0000.8040.782
Unnamed: 21.0001.0001.0001.0001.0000.7310.393
Unnamed: 41.0001.0001.0001.0001.0000.8060.742
Unnamed: 51.0001.0001.0001.0001.0001.0001.000
Unnamed: 61.0000.8040.7310.8061.0001.0000.994
Unnamed: 71.0000.7820.3930.7421.0000.9941.000

Missing values

2024-03-14T11:25:13.037748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:25:13.164333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-14T11:25:13.306912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

전북 지역자활센터 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7
0시군센터명센터장현원주소전화지정규모
1<NA><NA><NA>NaN<NA><NA>일자유형
2계19개소<NA>116<NA><NA><NA><NA>
3<NA>전북공석11전주시 덕진구 금암동 760-1226-03882008.10.1<NA>
4<NA>광역<NA>NaN<NA><NA><NA><NA>
5전주전주허종현7전주시 완산구 현무1길 21-15283-97661998.9.17확대
6<NA><NA><NA>NaN3층 (경원동3가 179-9)<NA><NA><NA>
7<NA>전주박준홍7전주시 완산구 팔달로 212-3232-83832000.8.24확대
8<NA>덕진<NA>NaN(경원동3가 33-9)<NA><NA><NA>
9<NA>전주조용희7전주시 덕진구 견훤왕궁로 277 YMCA 3층272-28452001.7. 1확대
전북 지역자활센터 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7
32<NA><NA>직무대행NaN<NA><NA><NA><NA>
33순창순창한승연5순창군 순창읍 교성2길 31653-09212002.1. 1기본
34<NA><NA><NA>NaN(교성리 61)<NA><NA><NA>
35고창고창제춘홍5고창군 고창읍 월곡14길 고창군민종합복지562-20142001.7. 1표준
36<NA><NA><NA>NaN회관 2층 (월곡리 620)<NA><NA><NA>
37부안부안장헌진5부안군 행안면 월륜길 5 현대자동차583-00452001.5.23표준
38<NA><NA><NA>NaNA/S 2층 (진동리 189-3)<NA><NA><NA>
39청소년전주허종현2전주시 완산구 현무1길 21-15288-90052001.8. 1-
40자 활<NA><NA>NaN<NA><NA><NA><NA>
41지원관정읍김복례2정읍시 상동 중앙로 3-1 2층537-71422005.7.15-